2022-04-18 ジョージア工科大学
・複雑な構造は複雑な物理的相互作用に依存しているため、研究者たちは、人工ニューラルネットワークモデル(複雑なパターンを数値表現に変換する数学的フレームワーク)を利用して、タンパク質の3次元的形状を予測し「見る」ことにした。
・『Nature Communications』誌に掲載された新しい論文では、ジョージア工科大学とオークリッジ国立研究所の研究者が、AlphaFold 2を用いて、個々のタンパク質だけでなく、複合体と呼ばれる機能タンパク質対の生物学的活性構造を予測することに成功しています。
<関連情報>
- https://research.gatech.edu/af2complex-researchers-leverage-deep-learning-predict-physical-interactions-protein-complexes
- https://www.nature.com/articles/s41467-022-29394-2
AF2Complexはディープラーニングにより多量体タンパク質の直接的な物理的相互作用を予測する AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
Mu Gao,Davi Nakajima An,Jerry M. Parks &Jeffrey Skolnick
Nature Communications Published: 01 April 2022
DO:Ihttps://doi.org/10.1038/s41467-022-29394-2
Abstract
Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.
